Archives - Page 4

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  HYBRID MACHINE LEARNING MODEL FOR EFFICIENT BOTNET ATTACK DETECTION IN IOT ENVIRONMENT

  Vol. 1 No. 1 (2024)

  Abstract

  The advent of machine learning has significantly transformed various sectors, introducing advanced predictive capabilities and intelligent systems. This paper presents a hybrid machine learning model combining Federated Learning (FL) and Explainable Artificial Intelligence (XAI). Federated Learning enhances privacy and security by allowing data to be trained across multiple decentralized devices without centralizing the data, while XAI provides interpretability and transparency to the model's decisions. Our proposed model leverages these technologies to ensure robust, secure, and understandable machine learning outcomes. The effectiveness of this hybrid model is demonstrated through extensive experiments, showing improved accuracy and interpretability without compromising user data privacy.Furthermore, the model addresses the growing concerns around data breaches and the lack of transparency in AI decision-making processes. By implementing Federated Learning, data remains localized on user devices, reducing the risk of exposure during data transfer. The integration of XAI techniques ensures that users and stakeholders can comprehend the rationale behind model predictions, fostering trust and compliance with regulatory standards. This combination is particularly beneficial for applications in sensitive areas such as healthcare, finance, and autonomous systems, where both data privacy and model transparency are paramount.

  Index Terms

  Hybrid Machine Learning, Federated Learning, Explainable AI (XAI), Data Privacy, Model Interpretability, Decentralized Training, Machine Learning Security, Transparent AI, Data Security, User Trust, AI in Healthcare, AI in Finance, AI Governance, SHAP, LIME, Model Transparency.

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  Real-Time Facial Analytics: A Deep Learning Approach to Gender, Age, and Emotion Recognition

  Vol. 2 No. 06 (2025)

  Abstract

  This project aims to develop a sophisticated real-time face recognition system capable of extracting comprehensive insights such as gender, age, and emotion, while incorporating statistical analysis. The project leverages advanced deep learning architectures, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, to achieve a multi-dimensional understanding of facial attributes.

  The Convolutional Neural Network is employed for its effectiveness in spatial feature extraction, enhancing the accuracy of gender and age estimation. Support Vector Machines contribute to refining classification boundaries, augmenting the overall precision of the recognition system. The inclusion of Long Short-Term Memory networks enables the model to capture temporal dependencies, facilitating nuanced emotion analysis in real-time scenarios.

  Additionally, the project incorporates statistical methods to provide valuable insights into the distribution and variability of demographic attributes and emotional states within the dataset. The holistic integration of these diverse approaches ensures a robust and efficient real-time face recognition system capable of delivering accurate and nuanced results across multiple dimensions. This project not only contributes to the advancement of facial recognition technology but also offers a valuable learning experience in the realm of deep learning and computer vision.

  Keywords

  Real-time face recognition, Gender estimation, Age estimation, Emotion analysis, Statistical analysis, Deep learning architectures, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, Spatial feature extraction, Temporal dependencies, Demographic attributes, Emotional states, Dataset analysis, Robust recognition system, Computer vision, Deep learning, Facial attributes, multi-dimensional understanding, Nuanced results.

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  Satellite Image Classification using Inverse Reinforcement Learning and Convolutional Neural Networks

  Vol. 1 No. 05 (2024)

  Abstract

  Satellite image classification plays a pivotal role in various fields such as agriculture, urban planning, and environmental monitoring. This project proposes a novel approach to satellite image classification by integrating Inverse Reinforcement Learning (IRL) with Convolutional Neural Networks (CNN). The methodology involves training the model to understand complex spatial patterns and features inherent in satellite imagery through the extraction of relevant features using CNNs.

  In the proposed framework, the model learns from expert demonstrations, mimicking the decision-making process of human experts in order to infer the underlying reward structure guiding their actions. This application of IRL allows the model to generalize and make informed predictions on unseen satellite data, contributing to enhanced classification accuracy.

  The project aims to compare the results obtained from the IRL-based CNN approach with the accuracy achieved by traditional satellite image classification algorithms. Commonly used algorithms such as Support Vector Machines (SVM), Random Forests, and conventional CNNs trained with supervised learning will be considered for comparison. The evaluation will be based on metrics such as precision, recall, and F1 score, providing a comprehensive analysis of the proposed methodology's effectiveness.

  The findings from this project are expected to shed light on the potential advantages and improvements offered by integrating inverse reinforcement learning techniques with CNNs in the context of satellite image classification. This research contributes to the growing field of remote sensing and machine learning applications, offering valuable insights for future developments in satellite image analysis.

  Index Terms

  Satellite image classification, Inverse Reinforcement Learning (IRL), Convolutional Neural Networks (CNN), Spatial patterns, Feature extraction, Expert demonstrations, Decision-making process, Reward structure, Generalization, Prediction, Classification accuracy, Support Vector Machines (SVM), Random Forests, Supervised learning, Evaluation metrics, Precision, Recall, F1 score, Remote sensing, Machine learning applications.

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  Shaping the Ultra-Connected Future: The Road to 6G

  Vol. 2 No. 09 (2025)

  Talk of “6G” is no longer an engineering thought experiment: it’s becoming a social and economic conversation. Vendors and standards bodies describe 6G as the platform that will move us from connected devices to connected intelligence and immersive experiences; governments are already funding research and patent drives; and citizens are thinking about towers, costs and health implications. This article surveys current views on 6G from industry white papers, policy bodies and early public reactions. It explains the potential benefits (new services,  resilience,  socio-economic  uplift),  explores  the  main  cost  levers  (spectrum, densification, energy), and outlines social and governance challenges (acceptance, equity, environmental impact). The aim is to give a balanced, readable synthesis of what people think, what’s at stake, and how societies might prepare for the ultra-connected infrastructures 6G promises.